DEA-C01 Data Operations and Support Practice Question
A data engineer is designing a data pipeline that ingests JSON files from an S3 bucket, transforms them using AWS Glue, and loads into Amazon Redshift. The data is updated daily, and the pipeline must handle late-arriving data from the previous day. Which approach minimizes reprocessing?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue job bookmarks to process only new files based on S3 event notifications.
AWS Glue job bookmarks track previously processed files and process only new or changed files, which handles late-arriving data without reprocessing all data. Option B uses Amazon Kinesis Data Firehose to stream data to Redshift; this is designed for real-time streaming, not a batch pipeline with daily updates, and does not inherently handle late-arriving data without custom logic. Option C (S3 versioning) can manage multiple versions but does not provide incremental processing for late-arriving data; it would require custom logic to determine which version to process. Option D (scheduling a full reload) would reprocess all data daily, which is inefficient and does not handle late-arriving data efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use AWS Glue job bookmarks to process only new files based on S3 event notifications.
Why this is correct
AWS Glue job bookmarks track previously processed files and process only new or changed files, which handles late-arriving data without reprocessing all data.
- ✗
Stream data using Amazon Kinesis Data Firehose to Redshift.
Why it's wrong here
Uses Amazon Kinesis Data Firehose, which is a streaming service, not a batch pipeline. It does not inherently handle late-arriving data without custom logic, and it is not a full reload approach.
- ✗
Enable S3 versioning and process only the latest version of each object.
Why it's wrong here
(S3 versioning) can manage multiple versions but does not provide incremental processing for late-arriving data; it would require custom logic to determine which version to process.
- ✗
Schedule a full reload of all data from S3 to Redshift each day.
Why it's wrong here
(scheduling a full reload) would reprocess all data daily, which is inefficient and does not handle late-arriving data efficiently.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,711-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.